Senior Forward Deployed Engineer - AI & Kubernetes (Toronto)

Tech Talent International (TTI) supplies technical talent to a variety of clients ranging from Fortune 100/500/1000 companies to startups, small and mid‑size organizations in Canada and the United States.

We are currently hiring a Senior Forward Deployed Engineer – AI & Kubernetes for our client in the Toronto area, a company that specializes in OS and secure toolkit development for AI and data stacks.

Job Summary

Senior Forward Deployed Engineer to join the client’s Platform team and work directly with strategic customers to transform business problems into production AI and data systems. The role is highly technical and customer‑facing, requiring a breadth of expertise in customer conversations, system design, data engineering, AI application development, Kubernetes, cloud infrastructure, and production support.

Responsibilities

• Embed with customer teams to understand business problems, workflows, data systems, constraints, and success metrics.
• Define what success looks like for each engagement, including target outcome, adoption path, production boundary, and measurable impact.
• Translate ambiguous customer needs into clear technical scopes, architectures, implementation plans, and production outcomes.
• Build and deploy AI and data applications on the platform, including agentic workflows, RAG systems, data pipelines, integrations, evaluations, and operational automation.
• Design and implement production data workflows across enterprise environments, including ingestion, transformation, orchestration, data quality, access control, and observability.
• Deploy and operate the platform in complex customer environments, including cloud, hybrid, on‑prem, private cloud, and air‑gapped infrastructure.
• Work hands‑on with Kubernetes, containers, networking, storage, identity, secrets, observability, and production troubleshooting.
• Partner with customer engineering, platform, data, and security teams to get systems live, governed, adopted, and measurable.
• Participate in Pager Duty‑based production support for customer deployments, including incident response, escalation, root‑cause analysis, and follow‑up remediation.
• Turn customer‑specific work into reusable patterns, playbooks, templates, and product feedback for the company.

Qualifications

• 8+ years of experience across software, data, platform, infrastructure, or AI engineering roles, including 3+ years building LLM/AI applications such as RAG, agents, evaluations, workflow automation, or production AI systems.
• 5+ years working with Kubernetes and cloud‑native infrastructure in production environments.
• Strong experience with major cloud platforms such as AWS, Azure, or GCP.
• Strong data engineering background, including pipelines, orchestration, transformation, data quality, access controls, and production data workflows.
• Experience with a contemporary data stack such as Spark, Airflow, Databricks, Snowflake, or similar.
• Experience building or deploying AI, data, or automation solutions in highly regulated or operationally complex industries (financial services, government, healthcare, energy, agriculture, supply chain, or industrial operations).
• Ability to apply AI to real‑world operational data such as sensor data, geospatial data, logistics data, ERP data, field operations data, or forecasting data.
• Proficiency in at least one production programming language such as Python, Go, TypeScript, Java, or Scala.
• Strong systems thinking across data, users, permissions, workflows, infrastructure, governance, and business processes.
• Excellent customer‑facing communication skills with engineers, operators, security teams, executives, and business owners.
• Strong ownership mindset: care about production rollout, adoption, reliability, operational handoff, and measurable impact.
• Willingness to travel to customer sites as needed.
• Plus: Experience in forward‑deployed, professional services, solutions architecture, customer engineering, field engineering, or technical consulting roles.
• Experience delivering outcome‑based customer engagements measured by adoption, operational improvement, or business impact.
• Experience with data engineering, machine learning, or data science workflows, including feature engineering, model training, experimentation, evaluation, or production ML systems.
• Experience with on‑prem, private cloud, regulated, hybrid, or air‑gapped deployments.
• Experience with infrastructure‑as‑code and production operations.
• Experience working with enterprise security, compliance, audit, access control, and governance requirements.
• Experience integrating AI or data systems with enterprise applications, internal APIs, data platforms, or customer‑specific operational tools.
• Experience leading senior technical stakeholders through architecture reviews, security reviews, implementation planning, and production‑readiness decisions.
• Ability to identify repeatable product and service opportunities from customer‑specific implementations.